class_docstr: Pipeline for text-to-image generation using Stable Diffusion.
  
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
  library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
  
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
      text_encoder ([`CLIPTextModel`]):
          Frozen text-encoder. Stable Diffusion uses the text portion of
          [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
          the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
      tokenizer (`CLIPTokenizer`):
          Tokenizer of class
          [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
      unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
      scheduler ([`SchedulerMixin`]):
          A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
          [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
      safety_checker ([`StableDiffusionSafetyChecker`]):
          Classification module that estimates whether generated images could be considered offensive or harmful.
          Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
      feature_extractor ([`CLIPFeatureExtractor`]):
          Model that extracts features from generated images to be used as inputs for the `safety_checker`.
docstr: Enable tiled VAE decoding.
  
  When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
  several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
docstr_tok: ["Enable","tiled","VAE","decoding",".","When","this","option","is","enabled",",","the","VAE","will","split","the","input","tensor","into","tiles","to","compute","decoding","and","encoding","in","several","steps",".","This","is","useful","to","save","a","large","amount","of","memory","and","to","allow","the","processing","of","larger","images","."]
code: def enable_vae_tiling(self):
      r"""
      Enable tiled VAE decoding.
  
      When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
      several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
      """
      self.vae.enable_tiling()
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Union
  import torch
  from packaging import version
  from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
  from diffusers.configuration_utils import FrozenDict
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring
  from diffusers.pipeline_utils import DiffusionPipeline
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
label_desc: Use image node_id 4 for calling the StableDiffusionPipeline obj's underlying member method code with example usage: obj.enable_vae_tiling() without return types